ABSTRACT
The aim of this research was to develop an artificial neural network (ANN) in order to design a nanoparticulate oral drug delivery system for insulin. The pH of polymer solution (X1), concentration ratio of polymer/insulin (X2) and polymer type (X3) in 3 level including methylated N-(4-N,N- dimethyl aminobenzyl) chitosan, methylated N-(4-pyridinyl) chitosan, and methylated N-(benzyl) chitosan are considered as the input values and the particle size, zeta potential, PdI, and entrapment efficiency (EE %) as output data. ANNs are employed to generate the best model to determining the relationships between input and response values. In this research, a multi-layer percepteron with different topologies has been tested in order to define the one with the best accuracy and performance. The optimization was used by minimizing the error between the predicted and observed values. Three training algorithms (Levenberg-Marquardt (LM), Bayesian-Regularization (BR), and Gradient Descent (GD)) were employed to train ANNs with various numbers of nodes, hidden layers and transfer functions by random selection. The accuracy of prediction data were assayed by the mean squared error (MSE).The ability of all algorithms was in the order: BR>LM>GD. Thus, BR was selected as the best algorithm.
